code: def __init__(self, **kwargs):
      super().__init__(**kwargs)
      self.conv1 = GCNConv(
          32, activation="elu", kernel_regularizer=l2(l2_reg)
      )
      self.conv2 = GCNConv(
          32, activation="elu", kernel_regularizer=l2(l2_reg)
      )
      self.flatten = GlobalSumPool()
      self.fc1 = Dense(512, activation="relu")
      self.fc2 = Dense(10, activation="softmax")
  
file_path: spektral/examples/other/graph_signal_classification_mnist.py
imports: import numpy
  import tensorflow
  from tensorflow.keras import Model
  from tensorflow.keras.layers import Dense
  from tensorflow.keras.losses import SparseCategoricalCrossentropy
  from tensorflow.keras.metrics import sparse_categorical_accuracy
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.regularizers import l2
  from spektral.data import MixedLoader
  from spektral.datasets.mnist import MNIST
  from spektral.layers import GCNConv, GlobalSumPool
  from spektral.utils.sparse import sp_matrix_to_sp_tensor
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: Model with example: obj = Net()